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While different in nature, data redundancy also occurs in database systems that have values repeated unnecessarily in one or more records or fields, ...
An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by the database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no ...
Data minimization is the principle of collecting, processing and storing only the necessary amount of personal information required for a specific purpose. The principle emanates from the realisation that processing unnecessary data is creating unnecessary risks for the data subject without creating any current benefit or value.
Database normalization is the process of structuring a relational database accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model .
Lax security: When there is a disaster, an organization's data and business processes become vulnerable. As such, security can be more important than the raw speed involved in a disaster recovery plan's RTO. The most critical consideration then becomes securing the new data pipelines: from new VPNs to the connection from offsite backup services.
Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points.
In a note to employees this week, Dorsey—Block's CEO and cofounder—said the company's internal reporting structure is getting an overhaul that will blow up the boundaries between various ...
The Data QC process uses the information from the QA process to decide to use the data for analysis or in an application or business process. General example: if a Data QC process finds that the data contains too many errors or inconsistencies, then it prevents that data from being used for its intended process which could cause disruption.